scientific-ensemble-methods

Combine XGBoost, LightGBM, and CatBoost models by building multi-stage ensembles with meta-models. This approach uses out-of-fold validation to improve predictive accuracy and outputs a stacking_meta.pkl file for deployment.

Compare gradient boosting models for classification and regression by evaluating their predictive performance across tabular data. This generates a boosting_comparison.csv file detailing the accuracy and stability of each algorithm.

Measure ensemble diversity for machine learning models using Q-statistic and disagreement metrics. This evaluates the variance across multiple algorithms and outputs the results into an ensemble_diversity.json file.

Yes, soft and hard voting ensembles are supported for tabular data classification and regression tasks. This applies voting across diverse base models to generate stable predictions and improve overall robustness.

Yes, you can leverage ToolUniverse OpenML references for benchmarking ensemble methods. This integration allows you to validate stacking, blending, and voting strategies against established datasets.